def funcPrunBranch(dfOrig, tree_model):
noPathCond = 0
#data set to hold set of candidate counter examples, refer to cand-set of prunBranch algorithm
with open('CandidateSetBranch.csv', 'w', newline='') as csvfile:
fieldnames = dfOrig.columns.values
writer = cv.writer(csvfile)
writer.writerow(fieldnames)
with open('param_dict.csv') as csv_file:
reader = cv.reader(csv_file)
paramDict = dict(reader)
dfRead = pd.read_csv('TestDataSMTMain.csv')
for row in range(0, dfRead.shape[0]):
if (paramDict['multi_label'] == 'True'):
funcgetPath4multiLbl(tree_model, dfOrig, row,
int(paramDict['no_of_params']))
else:
funcgetPath(tree_model, dfOrig, row)
fileCond = open('TreeOutput.txt', 'r')
first = fileCond.read(1)
if not first:
print('No Branch')
else:
noPathCond = util.file_len('ConditionFile.txt')
if (noPathCond == 'empty'):
return
for i in range(noPathCond):
funcAddCond2File(i)
os.system(r"z3 ToggleBranchSmt.smt2 > FinalOutput.txt")
satFlag = ReadZ3Output.funcConvZ3OutToData(dfOrig)
if (satFlag == True):
dfSmt = pd.read_csv('TestDataSMT.csv')
dataAppend = dfSmt.values
with open('CandidateSetBranch.csv', 'a',
newline='') as csvfile:
writer = cv.writer(csvfile)
writer.writerows(dataAppend)
def funcPrunInst(dfOrig, dnn_flag):
#data set to hold set of candidate counter examples, refer to cand-set of prunInst algorithm
with open('CandidateSetInst.csv', 'w', newline='') as csvfile:
fieldnames = dfOrig.columns.values
writer = cv.writer(csvfile)
writer.writerow(fieldnames)
with open('param_dict.csv') as csv_file:
reader = cv.reader(csv_file)
paramDict = dict(reader)
if (paramDict['multi_label'] == 'True'):
noClass = int(paramDict['no_of_class'])
else:
noClass = 1
#Getting the counter example pair (x, x') and saving it to a permanent storage
dfRead = pd.read_csv('TestDataSMTMain.csv')
dataRead = dfRead.values
#Combining loop in line 2 & 6 in a single loop
for j in range(0, dfRead.shape[0]):
for i in range(0, dfRead.columns.values.shape[0] - noClass):
#writing content of DecSmt.smt2 to another file named ToggleFeatureSmt.smt2
if (dnn_flag == True):
funcWrite2File('DNNSmt.smt2')
else:
funcWrite2File('DecSmt.smt2')
with open('ToggleFeatureSmt.smt2', 'r') as file:
text = file.read()
text = text.replace("(check-sat)", '')
text = text.replace("(get-model)", '')
with open('ToggleFeatureSmt.smt2', 'w') as file:
file.write(text)
fileTogFe = open('ToggleFeatureSmt.smt2', 'a')
name = str(dfRead.columns.values[i])
data_type = str(dfOrig.dtypes[i])
if ('int' in data_type):
digit = int(dataRead[j][i])
elif ('float' in data_type):
digit = float(dataRead[j][i])
digit = str(digit)
if ((int(paramDict['no_of_params']) == 1)
and (paramDict['multi_label'] == 'True')
and (paramDict['white_box_model'] == 'Decision tree')):
fileTogFe.write("(assert (not (= " + name + " " + digit +
"))) \n")
else:
fileTogFe.write("(assert (not (= " + name + str(j) + " " +
digit + "))) \n")
fileTogFe.write("(check-sat) \n")
fileTogFe.write("(get-model) \n")
fileTogFe.close()
os.system(r"z3 ToggleFeatureSmt.smt2 > FinalOutput.txt")
satFlag = ReadZ3Output.funcConvZ3OutToData(dfOrig)
#If sat then add the counter example to the candidate set, refer line 8,9 in prunInst algorithm
if (satFlag == True):
dfSmt = pd.read_csv('TestDataSMT.csv')
dataAppend = dfSmt.values
with open('CandidateSetInst.csv', 'a', newline='') as csvfile:
writer = cv.writer(csvfile)
writer.writerows(dataAppend)
def runPropCheck(self):
retrain_flag = False
MAX_CAND_ZERO = 5
count_cand_zero = 0
count = 0
satFlag = False
self.max_samples = int(self.paramDict['max_samples'])
self.no_of_params = int(self.paramDict['no_of_params'])
self.mul_cex = self.paramDict['mul_cex_opt']
self.deadline = int(self.paramDict['deadlines'])
white_box = self.paramDict['white_box_model']
start_time = time.time()
if white_box == 'DNN':
self.runWithDNN()
else:
while count < self.max_samples:
print('count is:', count)
tree = trainDecTree.functrainDecTree()
tree2Logic.functree2LogicMain(tree, self.no_of_params)
util.storeAssumeAssert('DecSmt.smt2')
util.addSatOpt('DecSmt.smt2')
os.system(r"z3 DecSmt.smt2 > FinalOutput.txt")
satFlag = ReadZ3Output.funcConvZ3OutToData(self.df)
if not satFlag:
if count == 0:
print(
'No CEX is found by the checker at the first trial'
)
return 0
elif (count != 0) and (self.mul_cex == 'True'):
dfCexSet = pd.read_csv('CexSet.csv')
if round(dfCexSet.shape[0] / self.no_of_params) == 0:
print('No CEX is found')
return 0
print('Total number of cex found is:',
round(dfCexSet.shape[0] / self.no_of_params))
self.addModelPred()
return round(dfCexSet.shape[0] / self.no_of_params)
elif (count != 0) and (self.mul_cex == 'False'):
print('No Cex is found after ' + str(count) +
' no. of trials')
return 0
else:
processCandCex.funcAddCex2CandidateSet()
processCandCex.funcAddCexPruneCandidateSet(tree)
processCandCex.funcCheckCex()
#Increase the count if no further candidate cex has been found
dfCand = pd.read_csv('Cand-set.csv')
if round(dfCand.shape[0] / self.no_of_params) == 0:
count_cand_zero += 1
if count_cand_zero == MAX_CAND_ZERO:
if self.mul_cex == 'True':
dfCexSet = pd.read_csv('CexSet.csv')
print(
'Total number of cex found is:',
round(dfCexSet.shape[0] /
self.no_of_params))
if round(dfCexSet.shape[0] /
self.no_of_params) > 0:
self.addModelPred()
return round(
dfCexSet.shape[0] / self.no_of_params) + 1
else:
print('No CEX is found by the checker')
return 0
else:
count = count + round(
dfCand.shape[0] / self.no_of_params)
data = dfCand.values
X = data[:, :-1]
y = data[:, -1]
if dfCand.shape[0] % self.no_of_params == 0:
arr_length = dfCand.shape[0]
else:
arr_length = dfCand.shape[0] - 1
testIndx = 0
while testIndx < arr_length:
temp_count = 0
temp_store = []
temp_add_oracle = []
for i in range(0, self.no_of_params):
if self.funcPrediction(X, dfCand,
testIndx) == y[testIndx]:
temp_store.append(X[testIndx])
temp_count += 1
testIndx += 1
else:
retrain_flag = True
temp_add_oracle.append(X[testIndx])
testIndx += 1
if temp_count == self.no_of_params:
if self.mul_cex == 'True':
with open('CexSet.csv', 'a',
newline='') as csvfile:
writer = cv.writer(csvfile)
writer.writerows(temp_store)
else:
print(
'A counter example is found, check it in CexSet.csv file: ',
temp_store)
with open('CexSet.csv', 'a',
newline='') as csvfile:
writer = cv.writer(csvfile)
writer.writerows(temp_store)
self.addModelPred()
return 1
else:
util.funcAdd2Oracle(temp_add_oracle)
if retrain_flag:
self.funcCreateOracle()
if (time.time() - start_time) > self.deadline:
print("Time out")
break
dfCexSet = pd.read_csv('CexSet.csv')
if (round(dfCexSet.shape[0] / self.no_of_params) >
0) and (count >= self.max_samples):
self.addModelPred()
print('Total number of cex found is:',
round(dfCexSet.shape[0] / self.no_of_params))
print(
'No. of Samples looked for counter example has exceeded the max_samples limit'
)
else:
print('No counter example has been found')